As an extension to its Data Lake Management Platform, Zaloni has introduced a machine-learning data matching engine, which leverages the data lake to create “golden” records and enable enriched data views for multiple use cases across business sectors. Zaloni’s data matching engine provides a new approach for creating an integrated, consistent view of data that is updated, efficiently maintained and can drive customer-facing applications. It addresses a gap in the marketplace for a simplified, much less expensive and faster-to-implement solution for data mastering.

Many master data records solutions are complex, inflexible, expensive and underperform for the cost,” said Ben Sharma, Zaloni’s CEO. “Zaloni’s data matching engine, which is offered as an extension to Zaloni’s Data Lake Management Platform, enables a practical, unique solution at a great value that will interest any organization that has a Customer or Product 360° initiative. For example, we implemented a Patient 360° project with one of our healthcare customers.”

With Zaloni’s Data Master extension, companies can leverage their data lake environment to achieve an enriched view of customer or product data for applications such as intelligent pricing, personalized marketing, smart alerts, customized recommendations, and more. Because it works directly in the data lake, organizations can capture and combine any data type, including unstructured data, which allows the engine to create a more robust single version of truth. Further, Zaloni’s data matching engine can use your sample data to train its machine-learning algorithms.

As companies mature into data-driven organizations, automation becomes the key to scaling and adding more use cases, improving the accuracy and quality of data, and accelerating business insights,” said Sharma. “Zaloni’s data matching engine provides this critical automation without significant new investment – a huge win for CIOs.”

Zaloni’s data matching engine is built on top of the powerful Zaloni Data Lake Management Platform and uses Spark machine-learning libraries and analytic approaches to integrate data silos. This includes probabilistic matching for record linkage, and advanced data clustering, and data classification techniques. In addition, Zaloni’s data matching extension uses reinforced learning techniques that enable customers to train the matching models based on live sample data. This approach provides maximum accuracy that may be adjusted as the data changes.

Zaloni’s data matching engine also leverages the Zaloni Data Lake Management Platform for metadata, data quality, scalability, user interface, and operational data pipelines for creating master records. In addition, Zaloni’s total, integrated package provides a clear advantage and faster time to value over more limited deduplication open source or point products that lack analytics data preparation capabilities such as joins and data profiling.

Zaloni’s data matching engine is currently in beta with select Zaloni customers. General availability is scheduled for fall 2017.

Resource Links:

Industry Perspectives

In this special guest feature, Brian D’alessandro, Director of Data Science at SparkBeyond, discusses how AI is a learning curve, and exploring opportunities within the technology further extends its potential to enable transformation and generate impact. It can shape workflows to drive efficiency and growth opportunities, while automating other workflows and create new business models. While AI empowers us with the ability to predict the future — we have the opportunity to change it. [READ MORE…]

Latest Video

White Papers

Darwin, a machine learning platform, accelerates data science at scale by automating the building and deployment of models. It provides a productive environment that empowers data scientist with a broad spectrum of experience to quickly prototype use cases and develop, tune, and implement machine learning applications in less time. Download the latest white paper from SparkCognition that compares how Darwin performs against other platforms in the market on the same datasets.